Edited by. Diego Andina. Technical University of Madrid (UPM), Spain. Duc Truong Pham. Manufacturing Engineering Center, Cardiff University, Cardiff ...
Computational Intelligence for Engineering and Manufacturing Edited by
Diego Andina Technical University of Madrid (UPM), Spain
Duc Truong Pham Manufacturing Engineering Center, Cardiff University, Cardiff
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN-10 ISBN-13 ISBN-10 ISBN-13
0-387-37450-7 (HB) 978-0-387-37450-5 (HB) 0-387-37452-3 (e-book) 978-0-387-37452-9 (e-book)
Published by Springer, P.O. Box 17, 3300 AA Dordrecht, The Netherlands. www.springer.com
Printed on acid-free paper
All Rights Reserved © 2007 Springer No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.
This book is dedicated to the memory of Roberto Carranza E., who induced the authors the enthusiasm to jointly prepare this book.
Soft Computing and its Applications in Engineering and Manufacture D. T. Pham, P. T. N. Pham, M. S. Packianather, A. A. Afify
Neural Networks Historical Review D. Andina, A. Vega-Corona, J. I. Seijas, J. Torres-García
Artificial Neural Networks D. T. Pham, M. S. Packianather, A. A. Afify
Application of Neural Networks D. Andina, A. Vega-Corona, J. I. Seijas, M. J. Alarcón
Radial Basis Function Networks and their Application in Communication Systems Ascensión Gallardo Antolín, Juan Pascual García, José Luis Sancho Gómez
Biological Clues for Up-to-Date Artificial Neurons Javier Ropero Peláez, Jose Roberto Castillo Piqueira
Support Vector Machines Jaime Gómez Sáenz de Tejada, Juan Seijas Martínez-Echevarría
Fractals as Pre-Processing Tool for Computational Intelligence Application Ana M. Tarquis, Valeriano Méndez, Juan B. Grau, José M. Antón, Diego Andina
D. Andina, J. I. Seijas, J. Torres-García, M. J. Alarcón, A. Tarquis, J. B. Grau and J. M. Antón work for Technical University of Madrid (UPM), Spain, where they form the Group for Automation and Soft Computing (GASC). D. T. Pham, P. T. N. Pham, M. S. Packianather and A. A. Afify work for Cardiff University . Javier Ropero Peláez, José Roberto Castillo Piqueira work for Escola Politecnica da Universidade de Sao Paulo Departamento de Engenharia de Telecomunicaçoes e Controle, Brazil. A. Gallardo Antolín, J. Pascual García and J. L. Sancho Gómez work for University Carlos III of Madrid, Spain, A. Vega-Corona, V. Méndez and J. Gómez Sáenz de Tejada work for University of Guanajuato, Mexico, Technical University of Madrid and Universidad Autónoma of Madrid, Spain, respectively.
This book presents a selected collection of contributions on a focused treatment of important elements of Computational Intelligence. Unlike traditional computing, Computational Intelligence (CI) is tolerant of imprecise information, partial truth and uncertainty. The principle components of CI that currently have frequent application in Engineering and Manufacturing are: Neural Networks (NN), fuzzy logic (FL) and Support Vector Machines (SVM). In CI, NN and SVM are concerned with learning, while FL with imprecision and reasoning. This volume mainly covers a key element of Computational Intelligence∗ learning. All the contributions in this volume have a direct relevance to neural network learning∗ from neural computing fundamentals to advanced networks such as Multilayer Perceptrons (MLP), Radial Basis Function Networks (RBF), and their relations with fuzzy set and support vector machines theory. The book also discusses different applications in Engineering and Manufacturing. These are among applications where CI have excellent potentials for use. Both novice and expert readers should find this book a useful reference in the field of Computational Intelligence. The editors and the authors hope to have contributed to the field by paving the way for learning paradigms to solve real-world problems D. Andina
This document has been produced with the financial assistance of the European Community, ALFA project II-0026-FA. The views expressed herein are those of the Authors and can therefore in no way be taken to reflect the official opinion of the European Community. The editors wish to thank Dr A. Afify of Cardiff University and Mr A. Jevtic of the Technical University of Madrid for their support and helpful comments during the revision of this text. The editors also wish to thank Nagib Callaos, President of the International Institute of Informatics and Systemics, IIIS, for his permission and freedom to reproduce in Chapters 2 and 4 of this book contents from the book by D.Andina and F.Ballesteros (Eds), “Recent Advances in Neural Networks” Ed. IIIS press, ILL, USA (2000).